Overview

Dataset statistics

Number of variables25
Number of observations375
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.4 KiB
Average record size in memory200.3 B

Variable types

Numeric22
Categorical3

Alerts

5 has constant value ""Constant
12 has constant value ""Constant
13 has constant value ""Constant
0 is highly overall correlated with 1 and 11 other fieldsHigh correlation
1 is highly overall correlated with 0 and 10 other fieldsHigh correlation
2 is highly overall correlated with 0 and 5 other fieldsHigh correlation
3 is highly overall correlated with 6 and 5 other fieldsHigh correlation
4 is highly overall correlated with 0 and 10 other fieldsHigh correlation
6 is highly overall correlated with 3 and 5 other fieldsHigh correlation
7 is highly overall correlated with 0 and 10 other fieldsHigh correlation
8 is highly overall correlated with 0 and 11 other fieldsHigh correlation
9 is highly overall correlated with 0 and 10 other fieldsHigh correlation
10 is highly overall correlated with 0 and 10 other fieldsHigh correlation
11 is highly overall correlated with 0 and 9 other fieldsHigh correlation
14 is highly overall correlated with 0 and 10 other fieldsHigh correlation
15 is highly overall correlated with 3 and 5 other fieldsHigh correlation
16 is highly overall correlated with 0 and 11 other fieldsHigh correlation
17 is highly overall correlated with 3 and 5 other fieldsHigh correlation
18 is highly overall correlated with 2 and 6 other fieldsHigh correlation
19 is highly overall correlated with 2 and 6 other fieldsHigh correlation
20 is highly overall correlated with 0 and 10 other fieldsHigh correlation
22 is highly overall correlated with 3 and 5 other fieldsHigh correlation
23 is highly overall correlated with 0 and 10 other fieldsHigh correlation
0 has unique valuesUnique
1 has unique valuesUnique
2 has unique valuesUnique
3 has unique valuesUnique
4 has unique valuesUnique
6 has unique valuesUnique
7 has unique valuesUnique
8 has unique valuesUnique
9 has unique valuesUnique
10 has unique valuesUnique
11 has unique valuesUnique
14 has unique valuesUnique
15 has unique valuesUnique
16 has unique valuesUnique
17 has unique valuesUnique
18 has unique valuesUnique
19 has unique valuesUnique
20 has unique valuesUnique
21 has unique valuesUnique
22 has unique valuesUnique
23 has unique valuesUnique

Reproduction

Analysis started2023-04-28 00:31:21.721776
Analysis finished2023-04-28 00:32:24.018907
Duration1 minute and 2.3 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0016877666
Minimum9.8487102 × 10-5
Maximum0.012226369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:24.219481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9.8487102 × 10-5
5-th percentile0.00011682443
Q10.00058428481
median0.0012693574
Q30.0018113637
95-th percentile0.0057052167
Maximum0.012226369
Range0.012127882
Interquartile range (IQR)0.0012270789

Descriptive statistics

Standard deviation0.0019297289
Coefficient of variation (CV)1.1433624
Kurtosis8.7444309
Mean0.0016877666
Median Absolute Deviation (MAD)0.00062259308
Skewness2.7672074
Sum0.63291246
Variance3.7238536 × 10-6
MonotonicityNot monotonic
2023-04-27T21:32:24.398592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002341487593 1
 
0.3%
0.0001111407493 1
 
0.3%
0.0001275798466 1
 
0.3%
0.0001164905704 1
 
0.3%
0.000152953845 1
 
0.3%
0.0001672979895 1
 
0.3%
0.0001414082001 1
 
0.3%
0.0001637978742 1
 
0.3%
0.0001177246826 1
 
0.3%
0.0001438272659 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
9.848710232 × 10-51
0.3%
9.869340586 × 10-51
0.3%
9.945534453 × 10-51
0.3%
9.976386179 × 10-51
0.3%
0.0001009781377 1
0.3%
0.0001035703609 1
0.3%
0.0001045243754 1
0.3%
0.0001102185845 1
0.3%
0.0001103173892 1
0.3%
0.0001109542428 1
0.3%
ValueCountFrequency (%)
0.01222636924 1
0.3%
0.01048563609 1
0.3%
0.01023340266 1
0.3%
0.01010078036 1
0.3%
0.01000532948 1
0.3%
0.009981979557 1
0.3%
0.009923139097 1
0.3%
0.009398933038 1
0.3%
0.008956142686 1
0.3%
0.008244518869 1
0.3%

1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.638763
Minimum0.25341216
Maximum615.37165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:24.585886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.25341216
5-th percentile0.72034771
Q16.4349798
median12.514188
Q398.65156
95-th percentile461.17423
Maximum615.37165
Range615.11824
Interquartile range (IQR)92.21658

Descriptive statistics

Standard deviation162.30615
Coefficient of variation (CV)1.6623126
Kurtosis1.370645
Mean97.638763
Median Absolute Deviation (MAD)9.1217604
Skewness1.7225108
Sum36614.536
Variance26343.286
MonotonicityNot monotonic
2023-04-27T21:32:24.760441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.484356589 1
 
0.3%
449.6366044 1
 
0.3%
438.5464706 1
 
0.3%
430.7907975 1
 
0.3%
418.47858 1
 
0.3%
424.9379922 1
 
0.3%
416.1801991 1
 
0.3%
429.9720493 1
 
0.3%
461.2202665 1
 
0.3%
440.2743886 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
0.2534121564 1
0.3%
0.3306238258 1
0.3%
0.352473162 1
0.3%
0.3804891536 1
0.3%
0.3879969896 1
0.3%
0.3947714346 1
0.3%
0.3983729893 1
0.3%
0.4235171227 1
0.3%
0.4506832568 1
0.3%
0.455243247 1
0.3%
ValueCountFrequency (%)
615.37165 1
0.3%
608.3797005 1
0.3%
552.5049949 1
0.3%
549.9847508 1
0.3%
542.7997297 1
0.3%
539.1411466 1
0.3%
526.3682006 1
0.3%
504.8361672 1
0.3%
498.7614759 1
0.3%
492.8941487 1
0.3%

2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.555465 × 108
Minimum1.5737578 × 108
Maximum2.2073485 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:24.952337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.5737578 × 108
5-th percentile2.1693995 × 108
Q13.4368709 × 108
median4.8198007 × 108
Q36.6389363 × 108
95-th percentile1.2043935 × 109
Maximum2.2073485 × 109
Range2.0499727 × 109
Interquartile range (IQR)3.2020654 × 108

Descriptive statistics

Standard deviation3.1167 × 108
Coefficient of variation (CV)0.56101514
Kurtosis4.3200458
Mean5.555465 × 108
Median Absolute Deviation (MAD)1.5147081 × 108
Skewness1.8379812
Sum2.0832994 × 1011
Variance9.7138188 × 1016
MonotonicityNot monotonic
2023-04-27T21:32:25.117877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396172138.4 1
 
0.3%
953637035.2 1
 
0.3%
711387122.6 1
 
0.3%
752222537.5 1
 
0.3%
530345587.4 1
 
0.3%
533626130.9 1
 
0.3%
642896964.3 1
 
0.3%
537102774.8 1
 
0.3%
791927229.5 1
 
0.3%
635455030.7 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
157375776.9 1
0.3%
161341169.5 1
0.3%
165549481.3 1
0.3%
181973085.8 1
0.3%
182119100.2 1
0.3%
192911176.3 1
0.3%
193734283.8 1
0.3%
198988838.8 1
0.3%
202907425.6 1
0.3%
203989209.2 1
0.3%
ValueCountFrequency (%)
2207348478 1
0.3%
1740446494 1
0.3%
1670919781 1
0.3%
1660973990 1
0.3%
1655837484 1
0.3%
1638803903 1
0.3%
1634604661 1
0.3%
1584020984 1
0.3%
1522106904 1
0.3%
1500581160 1
0.3%

3
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17387.356
Minimum9653.5467
Maximum34113.713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:25.292134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9653.5467
5-th percentile12628.101
Q114910.147
median16053.825
Q319108.078
95-th percentile25028.243
Maximum34113.713
Range24460.166
Interquartile range (IQR)4197.9307

Descriptive statistics

Standard deviation4025.0964
Coefficient of variation (CV)0.2314956
Kurtosis1.5840886
Mean17387.356
Median Absolute Deviation (MAD)1714.466
Skewness1.2558382
Sum6520258.4
Variance16201401
MonotonicityNot monotonic
2023-04-27T21:32:25.466756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13184.3097 1
 
0.3%
16283.4591 1
 
0.3%
15212.1742 1
 
0.3%
13664.2259 1
 
0.3%
14157.38052 1
 
0.3%
16180.58307 1
 
0.3%
15380.64842 1
 
0.3%
16054.31099 1
 
0.3%
15246.76087 1
 
0.3%
16161.25469 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
9653.54673 1
0.3%
10732.27921 1
0.3%
11631.35611 1
0.3%
11654.0376 1
0.3%
11659.5612 1
0.3%
11756.3546 1
0.3%
11758.58267 1
0.3%
11759.22898 1
0.3%
11759.67336 1
0.3%
11769.45568 1
0.3%
ValueCountFrequency (%)
34113.71314 1
0.3%
33742.98147 1
0.3%
32128.76275 1
0.3%
29569.6934 1
0.3%
28818.04579 1
0.3%
28108.19521 1
0.3%
27986.72503 1
0.3%
27560.56116 1
0.3%
26994.78097 1
0.3%
26634.31332 1
0.3%

4
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3309062
Minimum0.046558263
Maximum0.87593523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:25.647154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.046558263
5-th percentile0.055609257
Q10.22177242
median0.33287317
Q30.40835422
95-th percentile0.72975522
Maximum0.87593523
Range0.82937696
Interquartile range (IQR)0.18658179

Descriptive statistics

Standard deviation0.18620249
Coefficient of variation (CV)0.56270475
Kurtosis0.5205796
Mean0.3309062
Median Absolute Deviation (MAD)0.086470404
Skewness0.62178284
Sum124.08982
Variance0.034671367
MonotonicityNot monotonic
2023-04-27T21:32:25.821170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53311272 1
 
0.3%
0.05669050472 1
 
0.3%
0.0549376998 1
 
0.3%
0.05624848255 1
 
0.3%
0.05478118064 1
 
0.3%
0.05944960452 1
 
0.3%
0.05670899287 1
 
0.3%
0.05651172654 1
 
0.3%
0.05514710513 1
 
0.3%
0.05486848624 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
0.04655826317 1
0.3%
0.05162664701 1
0.3%
0.05197314081 1
0.3%
0.05202187068 1
0.3%
0.05281387503 1
0.3%
0.05292120232 1
0.3%
0.05315667982 1
0.3%
0.0534362454 1
0.3%
0.05348895455 1
0.3%
0.05379886466 1
0.3%
ValueCountFrequency (%)
0.8759352282 1
0.3%
0.8416224113 1
0.3%
0.8340321803 1
0.3%
0.8250902656 1
0.3%
0.8198786261 1
0.3%
0.8189001294 1
0.3%
0.8180699218 1
0.3%
0.8069787376 1
0.3%
0.8056533628 1
0.3%
0.799583615 1
0.3%

5
Categorical

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1.0
375 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1125
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 375
100.0%

Length

2023-04-27T21:32:25.978575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T21:32:26.175026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 375
100.0%

Most occurring characters

ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
66.7%
Other Punctuation 375
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 375
50.0%
0 375
50.0%
Other Punctuation
ValueCountFrequency (%)
. 375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1125
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

6
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68311.166
Minimum37726.187
Maximum134849.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:26.320505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum37726.187
5-th percentile49393.697
Q158287.582
median63108.318
Q375206.837
95-th percentile98529.015
Maximum134849.06
Range97122.869
Interquartile range (IQR)16919.255

Descriptive statistics

Standard deviation15967.608
Coefficient of variation (CV)0.23374814
Kurtosis1.6135116
Mean68311.166
Median Absolute Deviation (MAD)6826.82
Skewness1.259479
Sum25616687
Variance2.5496451 × 108
MonotonicityNot monotonic
2023-04-27T21:32:26.493431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51724.51291 1
 
0.3%
63507.27273 1
 
0.3%
59279.71689 1
 
0.3%
53163.62808 1
 
0.3%
55149.47028 1
 
0.3%
63182.44846 1
 
0.3%
59986.70171 1
 
0.3%
62678.91479 1
 
0.3%
59402.4648 1
 
0.3%
63062.9019 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
37726.18655 1
0.3%
42015.68832 1
0.3%
45633.18721 1
0.3%
45652.80818 1
0.3%
45727.35821 1
0.3%
45759.90018 1
0.3%
45981.57977 1
0.3%
45990.74248 1
0.3%
45993.77655 1
0.3%
45994.77683 1
0.3%
ValueCountFrequency (%)
134849.0558 1
0.3%
133428.8321 1
0.3%
126951.844 1
0.3%
116856.5579 1
0.3%
113905.8579 1
0.3%
111020.9701 1
0.3%
110452.8448 1
0.3%
108811.4538 1
0.3%
106708.5448 1
0.3%
104696.0106 1
0.3%

7
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2397255
Minimum1.9155884
Maximum2.5025627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:26.664631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.9155884
5-th percentile2.0222158
Q12.161096
median2.2462054
Q32.3252133
95-th percentile2.4270071
Maximum2.5025627
Range0.58697433
Interquartile range (IQR)0.16411729

Descriptive statistics

Standard deviation0.11867312
Coefficient of variation (CV)0.052985563
Kurtosis-0.32171588
Mean2.2397255
Median Absolute Deviation (MAD)0.085106387
Skewness-0.27594895
Sum839.89707
Variance0.014083309
MonotonicityNot monotonic
2023-04-27T21:32:26.844255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.263480781 1
 
0.3%
2.419350183 1
 
0.3%
2.363038531 1
 
0.3%
2.402658239 1
 
0.3%
2.296688548 1
 
0.3%
2.249023066 1
 
0.3%
2.337730412 1
 
0.3%
2.252270553 1
 
0.3%
2.385579573 1
 
0.3%
2.308661326 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
1.915588392 1
0.3%
1.923852705 1
0.3%
1.930636565 1
0.3%
1.950217524 1
0.3%
1.954323657 1
0.3%
1.957196217 1
0.3%
1.966640759 1
0.3%
1.969978337 1
0.3%
1.981043508 1
0.3%
1.991820188 1
0.3%
ValueCountFrequency (%)
2.502562718 1
0.3%
2.482764115 1
0.3%
2.469033996 1
0.3%
2.46433038 1
0.3%
2.462009313 1
0.3%
2.460521379 1
0.3%
2.460264835 1
0.3%
2.460258675 1
0.3%
2.458638156 1
0.3%
2.445097695 1
0.3%

8
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.168168
Minimum2.1059958
Maximum4.1043694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:27.035707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.1059958
5-th percentile2.3971634
Q12.9012289
median3.0583839
Q33.461605
95-th percentile4.0288667
Maximum4.1043694
Range1.9983736
Interquartile range (IQR)0.56037608

Descriptive statistics

Standard deviation0.47785895
Coefficient of variation (CV)0.15083132
Kurtosis-0.43525059
Mean3.168168
Median Absolute Deviation (MAD)0.2769028
Skewness0.2869987
Sum1188.063
Variance0.22834918
MonotonicityNot monotonic
2023-04-27T21:32:27.211772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.771328646 1
 
0.3%
4.05288417 1
 
0.3%
3.988147263 1
 
0.3%
4.023829901 1
 
0.3%
3.909313851 1
 
0.3%
3.865148758 1
 
0.3%
3.952700816 1
 
0.3%
3.870619811 1
 
0.3%
4.026993814 1
 
0.3%
3.937672913 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
2.105995839 1
0.3%
2.108498735 1
0.3%
2.122752865 1
0.3%
2.143012815 1
0.3%
2.171807987 1
0.3%
2.177651469 1
0.3%
2.192243323 1
0.3%
2.195900907 1
0.3%
2.196605659 1
0.3%
2.225531022 1
0.3%
ValueCountFrequency (%)
4.104369431 1
0.3%
4.103039962 1
0.3%
4.102380896 1
0.3%
4.101186152 1
0.3%
4.10075794 1
0.3%
4.091859264 1
0.3%
4.068698142 1
0.3%
4.067090486 1
0.3%
4.066904678 1
0.3%
4.061716663 1
0.3%

9
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00065652134
Minimum7.8691744 × 10-5
Maximum0.002434482
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:27.538400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7.8691744 × 10-5
5-th percentile8.4590944 × 10-5
Q10.00035843407
median0.00061965148
Q30.00080795401
95-th percentile0.001726096
Maximum0.002434482
Range0.0023557902
Interquartile range (IQR)0.00044951994

Descriptive statistics

Standard deviation0.00046408373
Coefficient of variation (CV)0.70688293
Kurtosis1.9023097
Mean0.00065652134
Median Absolute Deviation (MAD)0.00022016593
Skewness1.2728235
Sum0.2461955
Variance2.1537371 × 10-7
MonotonicityNot monotonic
2023-04-27T21:32:27.722187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001154049701 1
 
0.3%
8.42295766 × 10-51
 
0.3%
8.466693264 × 10-51
 
0.3%
8.558173431 × 10-51
 
0.3%
8.792180016 × 10-51
 
0.3%
8.663387912 × 10-51
 
0.3%
8.729823381 × 10-51
 
0.3%
8.552379966 × 10-51
 
0.3%
8.332938771 × 10-51
 
0.3%
8.466431343 × 10-51
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
7.869174399 × 10-51
0.3%
7.936875236 × 10-51
0.3%
8.029785983 × 10-51
0.3%
8.095455951 × 10-51
0.3%
8.181728557 × 10-51
0.3%
8.195021626 × 10-51
0.3%
8.210410127 × 10-51
0.3%
8.248032988 × 10-51
0.3%
8.261087191 × 10-51
0.3%
8.266947683 × 10-51
0.3%
ValueCountFrequency (%)
0.002434481964 1
0.3%
0.002196459654 1
0.3%
0.002151319779 1
0.3%
0.002101365214 1
0.3%
0.002076489458 1
0.3%
0.00206970287 1
0.3%
0.002066483066 1
0.3%
0.002010097624 1
0.3%
0.002001977686 1
0.3%
0.001973967707 1
0.3%

10
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98136349
Minimum0.24812561
Maximum1.6680771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:27.906452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.24812561
5-th percentile0.39150345
Q10.7616852
median0.88322904
Q31.1932057
95-th percentile1.64144
Maximum1.6680771
Range1.4199515
Interquartile range (IQR)0.4315205

Descriptive statistics

Standard deviation0.36623334
Coefficient of variation (CV)0.37318826
Kurtosis-0.47990936
Mean0.98136349
Median Absolute Deviation (MAD)0.18249214
Skewness0.44757942
Sum368.01131
Variance0.13412686
MonotonicityNot monotonic
2023-04-27T21:32:28.179778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5910528586 1
 
0.3%
1.641260343 1
 
0.3%
1.633342203 1
 
0.3%
1.629259647 1
 
0.3%
1.621165221 1
 
0.3%
1.626187269 1
 
0.3%
1.622913979 1
 
0.3%
1.628188574 1
 
0.3%
1.649093996 1
 
0.3%
1.636656476 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
0.2481256134 1
0.3%
0.2824503645 1
0.3%
0.2914888718 1
0.3%
0.3018972686 1
0.3%
0.3036508684 1
0.3%
0.3062910814 1
0.3%
0.3077182084 1
0.3%
0.3149660646 1
0.3%
0.32403928 1
0.3%
0.3255445285 1
0.3%
ValueCountFrequency (%)
1.668077101 1
0.3%
1.666012733 1
0.3%
1.663227079 1
0.3%
1.660415717 1
0.3%
1.656176897 1
0.3%
1.65458295 1
0.3%
1.653661442 1
0.3%
1.652538467 1
0.3%
1.652467166 1
0.3%
1.652090096 1
0.3%

11
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37856161
Minimum0.059986694
Maximum0.81948801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:28.651466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.059986694
5-th percentile0.080659486
Q10.28004192
median0.39639491
Q30.47485434
95-th percentile0.70733268
Maximum0.81948801
Range0.75950132
Interquartile range (IQR)0.19481242

Descriptive statistics

Standard deviation0.17709263
Coefficient of variation (CV)0.46780398
Kurtosis-0.26379733
Mean0.37856161
Median Absolute Deviation (MAD)0.1030273
Skewness0.083837483
Sum141.9606
Variance0.031361799
MonotonicityNot monotonic
2023-04-27T21:32:29.044375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5884009907 1
 
0.3%
0.1076308826 1
 
0.3%
0.09222655003 1
 
0.3%
0.1076802229 1
 
0.3%
0.07584319337 1
 
0.3%
0.05998669435 1
 
0.3%
0.08860434374 1
 
0.3%
0.06025563894 1
 
0.3%
0.09328901532 1
 
0.3%
0.07324961858 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
0.05998669435 1
0.3%
0.06025563894 1
0.3%
0.06882759962 1
0.3%
0.07155962626 1
0.3%
0.07259793666 1
0.3%
0.07311223602 1
0.3%
0.07324961858 1
0.3%
0.07387634006 1
0.3%
0.07406903666 1
0.3%
0.07411618912 1
0.3%
ValueCountFrequency (%)
0.8194880135 1
0.3%
0.7899985162 1
0.3%
0.7885378945 1
0.3%
0.7884601363 1
0.3%
0.7850505462 1
0.3%
0.7820009465 1
0.3%
0.7716573922 1
0.3%
0.770017131 1
0.3%
0.7691162856 1
0.3%
0.7565636211 1
0.3%

12
Categorical

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1.0
375 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1125
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 375
100.0%

Length

2023-04-27T21:32:29.214247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T21:32:29.359671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 375
100.0%

Most occurring characters

ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
66.7%
Other Punctuation 375
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 375
50.0%
0 375
50.0%
Other Punctuation
ValueCountFrequency (%)
. 375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1125
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

13
Categorical

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1.0
375 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1125
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 375
100.0%

Length

2023-04-27T21:32:29.467002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T21:32:29.637585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 375
100.0%

Most occurring characters

ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
66.7%
Other Punctuation 375
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 375
50.0%
0 375
50.0%
Other Punctuation
ValueCountFrequency (%)
. 375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1125
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 375
33.3%
. 375
33.3%
0 375
33.3%

14
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39802959
Minimum0.10785664
Maximum0.8762238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:29.788369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.10785664
5-th percentile0.11943878
Q10.3069697
median0.41362388
Q30.47554484
95-th percentile0.73788225
Maximum0.8762238
Range0.76836716
Interquartile range (IQR)0.16857513

Descriptive statistics

Standard deviation0.17175148
Coefficient of variation (CV)0.43150429
Kurtosis0.22151373
Mean0.39802959
Median Absolute Deviation (MAD)0.077286184
Skewness0.25592111
Sum149.2611
Variance0.02949857
MonotonicityNot monotonic
2023-04-27T21:32:29.962292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5751969268 1
 
0.3%
0.1203625156 1
 
0.3%
0.1183717062 1
 
0.3%
0.1199338481 1
 
0.3%
0.1200615458 1
 
0.3%
0.123642056 1
 
0.3%
0.1209681788 1
 
0.3%
0.120146467 1
 
0.3%
0.1186305901 1
 
0.3%
0.1182561073 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
0.1078566353 1
0.3%
0.1132185661 1
0.3%
0.1138191413 1
0.3%
0.1150284889 1
0.3%
0.1151936913 1
0.3%
0.1152128376 1
0.3%
0.1155817829 1
0.3%
0.1161630452 1
0.3%
0.1162274973 1
0.3%
0.1165846144 1
0.3%
ValueCountFrequency (%)
0.8762237969 1
0.3%
0.8423747424 1
0.3%
0.8351266581 1
0.3%
0.8266008693 1
0.3%
0.8212908075 1
0.3%
0.8205954211 1
0.3%
0.8196481459 1
0.3%
0.810330387 1
0.3%
0.8075040631 1
0.3%
0.8037010398 1
0.3%

15
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.81488
Minimum92.731762
Maximum183.39219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:30.153107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum92.731762
5-th percentile110.16554
Q1119.63282
median124.53262
Q3136.05155
95-th percentile156.73809
Maximum183.39219
Range90.66043
Interquartile range (IQR)16.418737

Descriptive statistics

Standard deviation14.700625
Coefficient of variation (CV)0.1141221
Kurtosis0.83300119
Mean128.81488
Median Absolute Deviation (MAD)7.1415632
Skewness0.95174631
Sum48305.581
Variance216.10837
MonotonicityNot monotonic
2023-04-27T21:32:30.321728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.2097166 1
 
0.3%
123.0879166 1
 
0.3%
119.6563368 1
 
0.3%
112.2348352 1
 
0.3%
116.0914883 1
 
0.3%
124.9492731 1
 
0.3%
120.7015557 1
 
0.3%
124.4101757 1
 
0.3%
119.3986152 1
 
0.3%
124.2643857 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
92.73176152 1
0.3%
100.78554 1
0.3%
104.3035957 1
0.3%
105.1088674 1
0.3%
105.3245349 1
0.3%
105.3924282 1
0.3%
105.4039758 1
0.3%
105.5999997 1
0.3%
105.8923819 1
0.3%
105.9119564 1
0.3%
ValueCountFrequency (%)
183.3921916 1
0.3%
182.9662838 1
0.3%
177.6639171 1
0.3%
171.1561622 1
0.3%
169.1771955 1
0.3%
166.7945071 1
0.3%
165.5266578 1
0.3%
164.9881178 1
0.3%
163.8333997 1
0.3%
161.6074984 1
0.3%

16
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0039521957
Minimum0.00020103969
Maximum0.027986523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:30.485855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00020103969
5-th percentile0.00023792978
Q10.0014009641
median0.0029804758
Q30.0043592655
95-th percentile0.012793213
Maximum0.027986523
Range0.027785483
Interquartile range (IQR)0.0029583015

Descriptive statistics

Standard deviation0.0045009967
Coefficient of variation (CV)1.1388598
Kurtosis9.4745598
Mean0.0039521957
Median Absolute Deviation (MAD)0.0014922673
Skewness2.8233304
Sum1.4820734
Variance2.0258972 × 10-5
MonotonicityNot monotonic
2023-04-27T21:32:30.653409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005868933396 1
 
0.3%
0.0002270260944 1
 
0.3%
0.0002618241339 1
 
0.3%
0.0002389100108 1
 
0.3%
0.0003104851851 1
 
0.3%
0.0003309208262 1
 
0.3%
0.0002798635988 1
 
0.3%
0.0003217236141 1
 
0.3%
0.0002339815196 1
 
0.3%
0.0002757992658 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
0.000201039689 1
0.3%
0.0002026143791 1
0.3%
0.0002118067672 1
0.3%
0.0002128472751 1
0.3%
0.0002159545346 1
0.3%
0.000219683246 1
0.3%
0.000220742471 1
0.3%
0.0002216142152 1
0.3%
0.0002237838649 1
0.3%
0.0002253336146 1
0.3%
ValueCountFrequency (%)
0.02798652311 1
0.3%
0.02749121627 1
0.3%
0.02681144504 1
0.3%
0.02587132373 1
0.3%
0.0223629468 1
0.3%
0.02228406514 1
0.3%
0.02198994324 1
0.3%
0.02007578069 1
0.3%
0.01915914237 1
0.3%
0.01849410878 1
0.3%

17
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69456.031
Minimum38598.459
Maximum136454.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:30.830749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum38598.459
5-th percentile50323.585
Q159371.62
median64199.961
Q376419.406
95-th percentile99911.628
Maximum136454.36
Range97855.899
Interquartile range (IQR)17047.786

Descriptive statistics

Standard deviation16101.114
Coefficient of variation (CV)0.23181736
Kurtosis1.5964754
Mean69456.031
Median Absolute Deviation (MAD)6840.7985
Skewness1.2559557
Sum26046012
Variance2.5924586 × 108
MonotonicityNot monotonic
2023-04-27T21:32:31.106681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52731.82122 1
 
0.3%
64688.74414 1
 
0.3%
60401.40388 1
 
0.3%
54232.99585 1
 
0.3%
56207.07167 1
 
0.3%
64297.54094 1
 
0.3%
61106.13565 1
 
0.3%
63790.77581 1
 
0.3%
60532.38225 1
 
0.3%
64201.2263 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
38598.4587 1
0.3%
42919.81558 1
0.3%
46526.92371 1
0.3%
46610.42106 1
0.3%
46632.85173 1
0.3%
46724.57469 1
0.3%
46934.14823 1
0.3%
46943.28402 1
0.3%
46946.13141 1
0.3%
46947.38185 1
0.3%
ValueCountFrequency (%)
136454.3573 1
0.3%
134975.442 1
0.3%
128528.4476 1
0.3%
118278.8254 1
0.3%
115273.6335 1
0.3%
112434.989 1
0.3%
111947.2187 1
0.3%
110232.544 1
0.3%
107977.6648 1
0.3%
106181.0678 1
0.3%

18
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19539991
Minimum8895289
Maximum51432191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:31.281966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8895289
5-th percentile11822916
Q115316539
median17041831
Q321981656
95-th percentile32629966
Maximum51432191
Range42536902
Interquartile range (IQR)6665116.3

Descriptive statistics

Standard deviation6946135.7
Coefficient of variation (CV)0.35548305
Kurtosis2.5630889
Mean19539991
Median Absolute Deviation (MAD)2768509.7
Skewness1.5245674
Sum7.3274967 × 109
Variance4.8248802 × 1013
MonotonicityNot monotonic
2023-04-27T21:32:31.448415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12873885.57 1
 
0.3%
17974924.02 1
 
0.3%
15934497.89 1
 
0.3%
13964406.51 1
 
0.3%
14115533.62 1
 
0.3%
16991344.27 1
 
0.3%
16170034.89 1
 
0.3%
16808564.56 1
 
0.3%
16163168.44 1
 
0.3%
17174990.87 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
8895289.045 1
0.3%
9648417.798 1
0.3%
10455810.64 1
0.3%
10523187.9 1
0.3%
10856077.72 1
0.3%
10856642.06 1
0.3%
10856827.47 1
0.3%
10857197.04 1
0.3%
10872494.58 1
0.3%
10920507.85 1
0.3%
ValueCountFrequency (%)
51432190.91 1
0.3%
50184616.19 1
0.3%
47365102.65 1
0.3%
41270947.87 1
0.3%
39558314.41 1
0.3%
38586209.54 1
0.3%
38269294.18 1
0.3%
37266020.87 1
0.3%
36391456.46 1
0.3%
36303414.9 1
0.3%

19
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7229489 × 109
Minimum2.2312736 × 109
Maximum1.9615675 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:31.621447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.2312736 × 109
5-th percentile2.8469369 × 109
Q14.028489 × 109
median4.7665357 × 109
Q36.6350734 × 109
95-th percentile1.1156296 × 1010
Maximum1.9615675 × 1010
Range1.7384402 × 1010
Interquartile range (IQR)2.6065844 × 109

Descriptive statistics

Standard deviation2.7811242 × 109
Coefficient of variation (CV)0.48596
Kurtosis3.6934358
Mean5.7229489 × 109
Median Absolute Deviation (MAD)9.7263348 × 108
Skewness1.7739158
Sum2.1461058 × 1012
Variance7.734652 × 1018
MonotonicityNot monotonic
2023-04-27T21:32:31.797039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3244547962 1
 
0.3%
5240211447 1
 
0.3%
4364624567 1
 
0.3%
3797892165 1
 
0.3%
3662082641 1
 
0.3%
4601094489 1
 
0.3%
4460644003 1
 
0.3%
4541138938 1
 
0.3%
4515238289 1
 
0.3%
4742039712 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
2231273568 1
0.3%
2283690669 1
0.3%
2420509613 1
0.3%
2450101032 1
0.3%
2615618948 1
0.3%
2616544695 1
0.3%
2616632310 1
0.3%
2617085946 1
0.3%
2621935542 1
0.3%
2674361235 1
0.3%
ValueCountFrequency (%)
1.961567536 × 10101
0.3%
1.880719481 × 10101
0.3%
1.779002685 × 10101
0.3%
1.454727164 × 10101
0.3%
1.36779132 × 10101
0.3%
1.352743847 × 10101
0.3%
1.314607812 × 10101
0.3%
1.291236861 × 10101
0.3%
1.287751804 × 10101
0.3%
1.274254411 × 10101
0.3%

20
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.181723
Minimum0.24900995
Maximum18.323471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:31.987530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.24900995
5-th percentile0.56923859
Q11.8736162
median2.584262
Q35.7852816
95-th percentile17.306696
Maximum18.323471
Range18.074461
Interquartile range (IQR)3.9116654

Descriptive statistics

Standard deviation5.5156521
Coefficient of variation (CV)1.0644436
Kurtosis0.73734363
Mean5.181723
Median Absolute Deviation (MAD)1.0685935
Skewness1.5112554
Sum1943.1461
Variance30.422419
MonotonicityNot monotonic
2023-04-27T21:32:32.155508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.175785973 1
 
0.3%
17.23191805 1
 
0.3%
17.16847083 1
 
0.3%
17.00850671 1
 
0.3%
16.68824723 1
 
0.3%
16.7927116 1
 
0.3%
16.71964787 1
 
0.3%
17.01202066 1
 
0.3%
17.42224778 1
 
0.3%
17.19174885 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
0.2490099507 1
0.3%
0.319065803 1
0.3%
0.3353576535 1
0.3%
0.3548992124 1
0.3%
0.3648553396 1
0.3%
0.3681936939 1
0.3%
0.3689788975 1
0.3%
0.39449496 1
0.3%
0.4010995609 1
0.3%
0.4119815577 1
0.3%
ValueCountFrequency (%)
18.32347131 1
0.3%
18.19336455 1
0.3%
18.085203 1
0.3%
18.00986803 1
0.3%
18.00937196 1
0.3%
17.9115286 1
0.3%
17.88794762 1
0.3%
17.87549273 1
0.3%
17.77211421 1
0.3%
17.67995404 1
0.3%

21
Real number (ℝ)

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0037463487
Minimum-0.14718615
Maximum0.058686736
Zeros0
Zeros (%)0.0%
Negative237
Negative (%)63.2%
Memory size3.1 KiB
2023-04-27T21:32:32.343979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-0.14718615
5-th percentile-0.017443073
Q1-0.0074773096
median-0.0020084136
Q30.0028680754
95-th percentile0.01094199
Maximum0.058686736
Range0.20587288
Interquartile range (IQR)0.010345385

Descriptive statistics

Standard deviation0.016738234
Coefficient of variation (CV)-4.4678793
Kurtosis26.031056
Mean-0.0037463487
Median Absolute Deviation (MAD)0.0053587945
Skewness-3.5951122
Sum-1.4048808
Variance0.00028016847
MonotonicityNot monotonic
2023-04-27T21:32:32.504194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003891838509 1
 
0.3%
-0.005102379039 1
 
0.3%
0.009589325578 1
 
0.3%
-0.007754544076 1
 
0.3%
0.003847563229 1
 
0.3%
-0.0001341297764 1
 
0.3%
0.0009573026562 1
 
0.3%
-0.003279279813 1
 
0.3%
-0.005849471176 1
 
0.3%
0.004168874012 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
-0.1471861472 1
0.3%
-0.1105161845 1
0.3%
-0.09386209029 1
0.3%
-0.09271799629 1
0.3%
-0.08244691816 1
0.3%
-0.07459632205 1
0.3%
-0.07196587426 1
0.3%
-0.0608834193 1
0.3%
-0.06075030877 1
0.3%
-0.02652191465 1
0.3%
ValueCountFrequency (%)
0.05868673552 1
0.3%
0.05440562514 1
0.3%
0.05418223101 1
0.3%
0.05173222002 1
0.3%
0.01609558581 1
0.3%
0.01486524852 1
0.3%
0.01425662325 1
0.3%
0.01421139185 1
0.3%
0.01405113333 1
0.3%
0.0140458482 1
0.3%

22
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17340.604
Minimum9649.2846
Maximum34114.388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:32.673279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9649.2846
5-th percentile12579.882
Q114755.149
median16007.735
Q319103.444
95-th percentile24960.827
Maximum34114.388
Range24465.103
Interquartile range (IQR)4348.2954

Descriptive statistics

Standard deviation4025.4058
Coefficient of variation (CV)0.23213757
Kurtosis1.6090352
Mean17340.604
Median Absolute Deviation (MAD)1686.5049
Skewness1.2560437
Sum6502726.6
Variance16203892
MonotonicityNot monotonic
2023-04-27T21:32:32.849913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13183.21084 1
 
0.3%
16060.73849 1
 
0.3%
14991.64915 1
 
0.3%
13451.42808 1
 
0.3%
13948.05522 1
 
0.3%
15969.12689 1
 
0.3%
15173.43183 1
 
0.3%
15841.17288 1
 
0.3%
15018.72328 1
 
0.3%
15941.20878 1
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
9649.284647 1
0.3%
10727.12541 1
0.3%
11574.86621 1
0.3%
11632.31714 1
0.3%
11633.95604 1
0.3%
11633.96958 1
0.3%
11647.29485 1
0.3%
11652.06581 1
0.3%
11657.20924 1
0.3%
11688.81443 1
0.3%
ValueCountFrequency (%)
34114.38789 1
0.3%
33742.16136 1
0.3%
32131.98665 1
0.3%
29568.98119 1
0.3%
28818.40326 1
0.3%
28108.58291 1
0.3%
27987.09281 1
0.3%
27557.37334 1
0.3%
26994.36589 1
0.3%
26540.45888 1
0.3%

23
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct375
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.273399 × 10-8
Minimum5.9003916 × 10-11
Maximum1.4328175 × 10-7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:33.020931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5.9003916 × 10-11
5-th percentile1.6772414 × 10-10
Q11.4983062 × 10-9
median2.9137753 × 10-9
Q32.2969807 × 10-8
95-th percentile1.0737877 × 10-7
Maximum1.4328175 × 10-7
Range1.4322275 × 10-7
Interquartile range (IQR)2.1471501 × 10-8

Descriptive statistics

Standard deviation3.7790999 × 10-8
Coefficient of variation (CV)1.6623126
Kurtosis0
Mean2.273399 × 10-8
Median Absolute Deviation (MAD)2.1238902 × 10-9
Skewness0
Sum8.5252462 × 10-6
Variance1.4281596 × 10-15
MonotonicityNot monotonic
2023-04-27T21:32:33.203175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.784519965 × 10-101
 
0.3%
1.046923749 × 10-71
 
0.3%
1.021101731 × 10-71
 
0.3%
1.003043597 × 10-71
 
0.3%
9.743761068 × 10-81
 
0.3%
9.894160567 × 10-81
 
0.3%
9.690246084 × 10-81
 
0.3%
1.001137242 × 10-71
 
0.3%
1.073894888 × 10-71
 
0.3%
1.025124977 × 10-71
 
0.3%
Other values (365) 365
97.3%
ValueCountFrequency (%)
5.900391612 × 10-111
0.3%
7.698170745 × 10-111
0.3%
8.206905771 × 10-111
0.3%
8.859223814 × 10-111
0.3%
9.034034577 × 10-111
0.3%
9.19176923 × 10-111
0.3%
9.275627019 × 10-111
0.3%
9.861077362 × 10-111
0.3%
1.049360751 × 10-101
0.3%
1.05997813 × 10-101
0.3%
ValueCountFrequency (%)
1.432817499 × 10-71
0.3%
1.416537601 × 10-71
0.3%
1.286440195 × 10-71
0.3%
1.280572115 × 10-71
0.3%
1.263842673 × 10-71
0.3%
1.255324111 × 10-71
0.3%
1.225583871 × 10-71
0.3%
1.175449169 × 10-71
0.3%
1.161304995 × 10-71
0.3%
1.147643642 × 10-71
0.3%

24
Real number (ℝ)

Distinct10
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7546667
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-04-27T21:32:33.351226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6098483
Coefficient of variation (CV)0.69509455
Kurtosis-0.57650251
Mean3.7546667
Median Absolute Deviation (MAD)2
Skewness0.77763304
Sum1408
Variance6.8113084
MonotonicityIncreasing
2023-04-27T21:32:33.460901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 93
24.8%
1 82
21.9%
3 35
 
9.3%
4 35
 
9.3%
5 35
 
9.3%
6 35
 
9.3%
8 25
 
6.7%
9 25
 
6.7%
7 5
 
1.3%
10 5
 
1.3%
ValueCountFrequency (%)
1 82
21.9%
2 93
24.8%
3 35
 
9.3%
4 35
 
9.3%
5 35
 
9.3%
6 35
 
9.3%
7 5
 
1.3%
8 25
 
6.7%
9 25
 
6.7%
10 5
 
1.3%
ValueCountFrequency (%)
10 5
 
1.3%
9 25
 
6.7%
8 25
 
6.7%
7 5
 
1.3%
6 35
 
9.3%
5 35
 
9.3%
4 35
 
9.3%
3 35
 
9.3%
2 93
24.8%
1 82
21.9%

Interactions

2023-04-27T21:32:20.596449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:25.635825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:28.418513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:30.725647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:33.001419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:35.407824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:37.613095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:40.024728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:42.563031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:45.223320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:47.724227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:50.506227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:52.962099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:55.721308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:58.358829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:00.935967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:03.608970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:06.117507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:08.952402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:11.889920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:14.725035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:17.629482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:20.719036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:25.823890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:28.523281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:30.913018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:33.104629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:35.506717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:37.724008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:40.138565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:42.686491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:45.335989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:47.845999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:50.623460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:53.075707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:55.837399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:58.479068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:01.061320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:03.725493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:06.248904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:09.075169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:12.020888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:14.874186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:17.767158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:20.838632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:25.935340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:28.627320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:31.009230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:33.211331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:35.607228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:37.828380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:40.254354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:42.805543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:45.453942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:47.968827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-27T21:32:03.371907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:05.887026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:08.707195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:11.619233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:14.446333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:17.355081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:20.341089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:23.148443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:28.323931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:30.625345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:32.912354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:35.305940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:37.521439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:39.930851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:42.461182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:45.113089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:47.615622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:50.402710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:52.854986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:55.605811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:31:58.149146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:00.829775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:03.503129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:06.014966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:08.839416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:11.767873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:14.578319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:17.506258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T21:32:20.474598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-27T21:32:33.617610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
01234678910111415161718192021222324
01.000-0.934-0.6460.0410.9240.056-0.765-0.9960.930-0.9330.8560.9260.1290.9900.053-0.004-0.056-0.9320.1530.065-0.934-0.362
1-0.9341.0000.456-0.062-0.991-0.0760.5550.949-0.9970.999-0.970-0.993-0.126-0.927-0.074-0.038-0.0040.999-0.125-0.0861.0000.384
2-0.6460.4561.0000.477-0.4090.4660.8740.621-0.4310.445-0.322-0.4170.375-0.6280.4690.5430.6050.441-0.2870.4600.4560.308
30.041-0.0620.4771.0000.1051.0000.063-0.0510.087-0.0760.0850.1000.9900.0411.0000.9940.978-0.079-0.2630.999-0.0620.002
40.924-0.991-0.4090.1051.0000.119-0.520-0.9370.998-0.9940.9761.0000.1660.9220.1170.0820.049-0.9950.1060.129-0.991-0.366
60.056-0.0760.4661.0000.1191.0000.051-0.0660.101-0.0900.0980.1140.9920.0561.0000.9920.976-0.093-0.2631.000-0.076-0.007
7-0.7650.5550.8740.063-0.5200.0511.0000.748-0.5360.547-0.405-0.526-0.049-0.7410.0540.1370.2120.546-0.2190.0450.5550.351
8-0.9960.9490.621-0.051-0.937-0.0660.7481.000-0.9440.947-0.873-0.939-0.136-0.980-0.062-0.0070.0440.947-0.157-0.0750.9490.369
90.930-0.997-0.4310.0870.9980.101-0.536-0.9441.000-0.9990.9750.9990.1500.9270.0990.0630.030-0.9990.1190.111-0.997-0.379
10-0.9330.9990.445-0.076-0.994-0.0900.5470.947-0.9991.000-0.973-0.996-0.139-0.928-0.087-0.051-0.0171.000-0.126-0.0990.9990.385
110.856-0.970-0.3220.0850.9760.098-0.405-0.8730.975-0.9731.0000.9760.1330.8590.0960.0730.052-0.9730.0990.108-0.970-0.368
140.926-0.993-0.4170.1001.0000.114-0.526-0.9390.999-0.9960.9761.0000.1610.9240.1110.0760.043-0.9970.1090.123-0.993-0.371
150.129-0.1260.3750.9900.1660.992-0.049-0.1360.150-0.1390.1330.1611.0000.1260.9910.9720.945-0.143-0.2430.993-0.126-0.035
160.990-0.927-0.6280.0410.9220.056-0.741-0.9800.927-0.9280.8590.9240.1261.0000.052-0.002-0.051-0.9270.1400.065-0.927-0.369
170.053-0.0740.4691.0000.1171.0000.054-0.0620.099-0.0870.0960.1110.9910.0521.0000.9930.976-0.091-0.2631.000-0.074-0.006
18-0.004-0.0380.5430.9940.0820.9920.137-0.0070.063-0.0510.0730.0760.972-0.0020.9931.0000.994-0.055-0.2790.992-0.0380.016
19-0.056-0.0040.6050.9780.0490.9760.2120.0440.030-0.0170.0520.0430.945-0.0510.9760.9941.000-0.021-0.2920.974-0.0040.043
20-0.9320.9990.441-0.079-0.995-0.0930.5460.947-0.9991.000-0.973-0.997-0.143-0.927-0.091-0.055-0.0211.000-0.121-0.1030.9990.384
210.153-0.125-0.287-0.2630.106-0.263-0.219-0.1570.119-0.1260.0990.109-0.2430.140-0.263-0.279-0.292-0.1211.000-0.263-0.125-0.329
220.065-0.0860.4600.9990.1291.0000.045-0.0750.111-0.0990.1080.1230.9930.0651.0000.9920.974-0.103-0.2631.000-0.086-0.012
23-0.9341.0000.456-0.062-0.991-0.0760.5550.949-0.9970.999-0.970-0.993-0.126-0.927-0.074-0.038-0.0040.999-0.125-0.0861.0000.384
24-0.3620.3840.3080.002-0.366-0.0070.3510.369-0.3790.385-0.368-0.371-0.035-0.369-0.0060.0160.0430.384-0.329-0.0120.3841.000

Missing values

2023-04-27T21:32:23.368143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-27T21:32:23.811725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

0123456789101112131415161718192021222324
00.0023412.4843573.961721e+0813184.3097040.5331131.051724.5129102.2634812.7713290.0011540.5910530.5884011.01.00.575197112.2097170.00586952731.8212201.287389e+073.244548e+091.1757860.00389213183.2108395.784520e-101.0
10.0017613.5792115.476226e+0815717.3902500.4829131.061734.8578462.3035832.8859870.0010100.6556700.5594001.01.00.535588122.7308590.00413662856.5988951.666798e+074.549369e+091.4108370.00924715714.2137408.333754e-101.0
20.00138211.1400154.537162e+0817196.3659850.3524371.067619.4390032.2445403.0541070.0006630.8594760.4297901.01.00.429125129.1926300.00369268770.2761821.886087e+075.329816e+092.4015810.00416117190.7933442.593816e-091.0
30.0013323.5472108.804298e+0815732.8151670.4866581.061787.8152202.4210492.9967130.0010150.6544260.5867741.01.00.538601120.6011260.00286862946.1101841.767228e+075.295252e+091.399464-0.01718715736.5829248.259244e-101.0
40.0013266.7301925.843474e+0816681.1494150.4051011.065546.5594482.3078483.0175310.0007960.7699490.4976331.01.00.473132126.4727280.00324966704.8006341.829152e+075.200287e+091.9097670.01197316675.5056631.567043e-091.0
50.0011903.3924271.253675e+0917221.0579390.4956331.067671.1779052.4690343.0335980.0010350.6463160.6009821.01.00.545715125.7121380.00227368902.7035032.031699e+076.362667e+091.362016-0.01996317225.8098807.898851e-101.0
60.0015948.9373153.127832e+0813781.8040560.3579551.054126.4028562.1702132.9481300.0006960.8255390.4255581.01.00.434958115.4047810.00388055119.8983761.361420e+073.471202e+092.237935-0.00166413778.6418502.080944e-091.0
70.0011847.1512256.521523e+0816249.7384480.3976481.063819.6030732.3669213.0857710.0007740.7821030.5072281.01.00.467054123.2503930.00317164977.0450771.834143e+075.535238e+091.9695070.01366516243.4363411.665075e-091.0
80.0011543.9987491.349822e+0917355.4113350.4850601.068201.7027892.4827643.0736190.0010010.6698740.5914651.01.00.536925125.4748660.00302069437.7156232.080218e+076.628725e+091.442459-0.01872917359.4094769.310597e-101.0
90.0017687.4425143.478130e+0815388.6683170.3891601.060482.2874472.1804472.9151330.0007630.7881190.4500711.01.00.460239122.6180510.00426061543.1500041.577285e+074.120326e+092.0138440.00432615384.8848111.732898e-091.0
0123456789101112131415161718192021222324
3650.000112498.7614767.964831e+0814979.2050060.0519731.058291.4194192.3970364.0560520.0000791.6660130.0902501.01.00.113819118.0699640.00022259414.0560301.576261e+074.375554e+0918.1933650.00455514729.7460451.161305e-079.0
3660.000122425.4448401.007086e+0919618.2305290.0573051.076767.9480852.3862174.0030610.0000861.6267000.1033251.01.00.121397136.5187650.00025478061.0413332.327099e+077.205138e+0916.852946-0.01532119409.9656619.905962e-089.0
3670.000147433.8452444.572969e+0811850.5322130.0593511.045998.8131162.3156753.9381070.0000861.6312130.0789301.01.00.122699105.3924280.00031646966.3759611.100232e+072.699534e+0916.9914700.00169711633.9560421.010156e-079.0
3680.000113460.0902809.148823e+0815780.7269160.0548151.061513.4079572.4259324.0588430.0000851.6413650.1106991.01.00.118486120.8299440.00022862676.2480211.731952e+075.066935e+0917.232257-0.01100115554.9834041.071264e-079.0
3690.000100552.5049951.634605e+0923471.9912560.0554221.091890.4341772.4586384.1030400.0000821.6561770.1182021.01.00.118209148.7064810.00021693342.2039733.067744e+071.049333e+1017.875493-0.00469323198.5864991.286440e-079.0
3700.0024082.4973044.347445e+0814563.9355260.5492561.057189.0227922.2535062.7530890.0011610.5947940.5902641.01.00.588648118.5871990.00486458249.1865461.474904e+073.834998e+091.1478490.00453314562.5987585.814665e-1010.0
3710.0062200.3983735.432250e+0819326.8112240.8189001.076108.5654852.2246542.3579120.0020700.3077180.7884601.01.00.820595137.1637660.01414777319.8975552.237578e+076.641421e+090.368194-0.01260219330.9463729.275627e-1110.0
3720.0059150.9809961.929112e+0811631.3561090.6874761.045633.1872132.1177262.4243820.0015710.4405900.6667171.01.00.702208106.4286670.01366146526.9237111.045581e+072.420510e+090.683725-0.00200811632.3171372.284130e-1010.0
3730.0026102.2340574.888503e+0815518.8797810.5669181.060977.0484752.2881102.7574720.0012060.5759230.6125931.01.00.603004122.0720630.00688462085.2555951.644176e+074.528773e+091.079742-0.01097015521.7090575.201728e-1010.0
3740.0026331.4056651.056409e+0923483.3391030.6563761.092535.9044242.3621162.7303760.0014470.4938390.6761221.01.00.676847150.4765200.00614293947.3944753.031877e+071.008084e+100.798436-0.01087223487.6727853.272918e-1010.0